PlusPCA: A clustering function

In MSeidelFed/RandodiStats_package: Random distributions and their univariate and multivariate statistics

A clustering function

Description

This function allows you to rationalize the clustering method selected.

Usage

PlusPCA(
  inMat = distribution_test_mat(),
  Km = NULL,
  returnValuesPlot = T,
  returnTreatmentValues = T,
  pamClust = T,
  Kpam
)

Arguments

inMat	Defaults to a randomly generated matrix from the distribution_test_mat function.
Km	must be a result object from the function KmeansPlus().
returnValuesPlot	if TRUE, returns all relevant PCA stats to manually build the plots.
returnTreatmentValues	if TRUE, returns treatment values, if FALSE, returns features values.

Examples

test <- PlusPCA()

MSeidelFed/RandodiStats_package documentation built on July 31, 2022, 3 a.m.